diff --git a/docs/Summary.org b/docs/Summary.org
index b83f1d5..3e18e2f 100644
--- a/docs/Summary.org
+++ b/docs/Summary.org
@@ -75,6 +75,8 @@ def parse_data(source, action):
     return test_data, target_data
 #+end_src
 
+#+RESULTS:
+
 
 A continuación, mostraremos cada uno de los pasos que realizamos para obtener el /dataset/ final:
 
@@ -203,3 +205,130 @@ Datos objetivo:
 4    1
 Name: Severity, dtype: int64
 #+end_example
+** Configuración de algoritmos
+
+Elegimos 5 algoritmos distintos:
+
+1. Naive Bayes
+2. Linear Support Vector Classification
+3. K Nearest Neighbors
+4. Árbol de decisión
+5. Perceptrón multicapa (red neuronal)
+
+Procedemos  a evaluar el rendimiento de cada algoritmo, usando las siguientes métricas:
+
+- Accuracy score
+- Matriz de confusión
+- Cross validation score
+- Area under the curve (AUC)
+
+Vamos a realizar 2 ejecuciones por algoritmo, para evaluar las diferencias que obtenemos según el preprocesado utilizado (eliminación de valores nulos o imputación).
+
+La implementación se encuentra en el archivo /processing.py/, cuyo contenido mostramos  a continuación:
+
+#+begin_src python
+from numpy import mean
+from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score
+from sklearn.model_selection import cross_val_score
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neural_network import MLPClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import scale
+from sklearn.svm import LinearSVC
+from sklearn.tree import DecisionTreeClassifier
+
+from sys import argv
+
+from preprocessing import parse_data, split_k_sets
+
+
+def choose_model(model):
+    if model == "gnb":
+        return GaussianNB()
+    elif model == "svc":
+        return LinearSVC(random_state=42)
+    elif model == "knn":
+        return KNeighborsClassifier(n_neighbors=10)
+    elif model == "tree":
+        return DecisionTreeClassifier(random_state=42)
+    elif model == "neuralnet":
+        return MLPClassifier(hidden_layer_sizes=10)
+    else:
+        print("Unknown model selected. The choices are: ")
+        print("gnb: Gaussian Naive Bayes")
+        print("svc: Linear Support Vector Classification")
+        print("knn: K-neighbors")
+        print("tree: Decision tree")
+        print("neuralnet: MLP Classifier")
+        exit()
+
+
+def predict_data(data, target, model):
+    model = choose_model(model)
+    if model == "knn":
+        data = scale(data)
+    accuracy_scores = []
+    confusion_matrices = []
+    auc = []
+    for train_index, test_index in split_k_sets(data):
+        model.fit(data.iloc[train_index], target.iloc[train_index])
+        prediction = model.predict(data.iloc[test_index])
+        accuracy_scores.append(accuracy_score(target.iloc[test_index], prediction))
+        confusion_matrices.append(confusion_matrix(target.iloc[test_index], prediction))
+        auc.append(roc_auc_score(target.iloc[test_index], prediction))
+    cv_score = cross_val_score(model, data, target, cv=10)
+    evaluate_performance(
+        confusion_matrix=mean(confusion_matrices, axis=0),
+        accuracy=mean(accuracy_scores),
+        cv_score=mean(cv_score),
+        auc=mean(auc),
+    )
+
+
+def evaluate_performance(confusion_matrix, accuracy, cv_score, auc):
+    print("Accuracy Score: " + str(accuracy))
+    print("Confusion matrix: ")
+    print(str(confusion_matrix))
+    print("Cross validation score: " + str(cv_score))
+    print("AUC: " + str(auc))
+#+end_src
+
+** Resultados obtenidos
+*** Naives Bayes
+
+Los resultados que obtenemos son los siguientes:
+
+#+CAPTION: Naive Bayes
+[[./assets/gnb.png]]
+*** Linear SVC
+
+Los resultados que obtenemos son los siguientes:
+
+#+CAPTION: Linear SVC con eliminación
+[[./assets/svc_drop.png]]
+
+#+CAPTION: Linear SVC con imputación
+[[./assets/svc_fill.png]]
+*** KNN
+Antes de ejecutar este algoritmo, normalizamos los datos dado que el /KNN/ es un algoritmo basado en distancia.
+
+Los resultados que obtenemos son los siguientes:
+
+#+CAPTION: KNN
+[[./assets/knn.png]]
+*** Árbol de decisión
+
+Los resultados que obtenemos son los siguientes:
+
+#+CAPTION: Árbol de decisión
+[[./assets/tree.png]]
+*** Perceptrón multicapa
+
+Los resultados que obtenemos son los siguientes:
+
+#+CAPTION: Perceptrón multicapa con eliminación
+[[./assets/neuralnet_drop.png]]
+
+#+CAPTION: Perceptrón multicapa con imputación
+[[./assets/neuralnet_fill.png]]
+** Análisis de resultados
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